ZHU-THESIS-2021.Pdf

AN EVALUATION OF HYPERALIGNMENT ON REPRODUCIBILITY AND PREDICTION ACCURACY FOR FMRI DATA by Xuemin Zhu A thesis submitted to Johns Hopkins University in conformity with the requirements for the degree of Master of Science in Engineering Baltimore, Maryland May, 2021 © 2021 by Xuemin Zhu All rights reserved Abstract Functional magnetic resonance imaging (fMRI) is a neuroimaging technique which measures a person’s brain activity using changes in the blood flow in response to neural activity. Recently, resting state fMRI (rs-fMRI)has become a ubiquitous tool for measuring connectivity and examining the functional architecture of the human brain. Here, we used a publicly available rs-fMRI dataset to investigate the performance of the hyperalignment algorithm, on several fMRI analyses. The research employs the use of the image intra-class correlation coefficient and functional connectome fingerprinting to evaluate the reproducibility of both the unaligned and hyperaligned data, and developed a predictive model to investigate whether hyperalignment improves the prediction of certain behavioral measures. Overall, our results illustrate the utility of the hyperalignment algorithm for studying inter-individual variation in brain activity. ii Thesis Committee Primary Readers Martin Lindquist (Primary Advisor) Professor Department of Biostatistics Johns Hopkins Bloomberg School of Public Health Brian Caffo Professor Department of Biostatistics Johns Hopkins Bloomberg School of Public Health James J. Pekar Professor Department of Radiology and Radiological Science Johns Hopkins School of Medicine iii Acknowledgments Throughout the writing of this thesis I have received a great deal of support and assistance. First, I am eternally grateful and indebted to my thesis advisor, Prof. Mar- tin Lindquist, whose patience, support and understanding were invaluable in such a difficult time like this. I really appreciate everything that youhelped me both in academics and in life. I would like to thank Farzad Vasheghani-Farahani for his wonderful col- laboration. Without his selfless help, this paper would not have been inits present form. I would also like to thank my thesis committee members for their thought- ful and insightful comments, advice and help. Finally, I would like to thank my dear family for their love and great faith in me for many years.I also want to thank my friends who have given me help and time to listen to my opinions and help me solve my problems during this difficult time. iv Table of Contents Abstract ii Thesis Committee iii Acknowledgments iv Table of Contentsv List of Tables vii List of Figures viii 1 Introduction1 1.1 Background . .1 1.1.1 A brief history of neuroimaging . .1 1.1.2 Multivariate pattern analysis of fMRI data . .4 1.1.3 Hyperalignment . .5 1.2 Problem Statement . .6 1.3 Purpose of the Study . .7 1.3.1 Aim . .7 1.3.2 Objective . .7 v 1.3.3 Procedure . .7 1.3.4 Significance of the study . .8 1.3.5 Structure of the paper . .9 2 Literature Review 11 2.1 Neuroimaging . 11 2.2 Hyperalignment for fMRI Data . 13 3 Methodology 20 3.1 Human Connectome Project Data . 20 3.2 Hyperalignment . 21 3.3 Measures of Reliability: ICC, I2C2 and fingerprinting . 23 3.3.1 I2C2 . 23 3.3.2 Fingerprinting . 25 3.4 Predictive Modeling Approaches . 26 4 Results 29 4.1 Research Design . 29 4.2 Reliability . 30 4.3 Predictive Modeling . 33 5 Discussion and Conclusion 40 5.1 Discussion . 40 5.2 Limitations and future directions . 41 5.3 Conclusion . 42 vi List of Tables 4.1 Reliability Results for Unaligned Data of the Whole Brain . 30 4.2 I2C2 Results . 31 4.3 A table showing the fingerprinting accuracy of the aligned and unaligned data . 32 4.4 Evaluation Results . 37 4.5 AUC value for predicting gender . 38 vii List of Figures 4.1 Scatter plots of predicted values vs actual values for age. The results for the unaligned data are show to the left and the results for the aligned data to the right. 34 4.2 Scatter plots of predicted values vs actual values for BMI. The results for the unaligned data are show to the left and the results for the aligned data to the right. 35 4.3 Scatter plots of predicted values vs actual values for fluid intel- ligence. The results for the unaligned data are show to the left and the results for the aligned data to the right. 36 4.4 Scatter plots of predicted values vs actual values for depression score. The results for the unaligned data are show to the left and the results for the aligned data to the right. 37 viii Chapter 1 Introduction 1.1 Background 1.1.1 A brief history of neuroimaging The first example of brain imaging dates back tothe 19th century in the form of the human circulation balance invented by the Italian physiologist Angelo Mosso. This precursor to modern neuroimaging devices was able to measure the redistribution of blood in the brain during emotional or intellectual activity. In this application, Mosso discovered important variables such as the signal to noise ratio, which are essential properties of modern methods of brain imaging. According to Savoy, 2001, the beginning of modern neuroimaging dates back to the period between 1895-1973. During this time, Wilhelm Roentgen demonstrated the first-ever radiograph, which provided a gateway to modern forms of diagnosis. However, given the fact that most of the brain is made up of soft tissue, it was virtually impossible to view most of its parts using a standard radiograph. Therefore, in the early 20th century Walter Dandy, an American neurosurgeon, introduced ventriculography. This technique 1 involved taking images of the ventricular system within the brain after the injection of filtered air into the lateral ventricles. Access to the ventricles was obtained by drilling a hole in the skull of the patients. Despite its success in being able to improve upon the performance of the radiograph, the technique presented a series of risks that jeopardized the health of the patients. These included potential infractions, threatening changes in the brain pressure, and possible hemorrhaging. Nevertheless, ventriculography proved successful in reducing the margin of error when performing neurosurgery. With the continued discovery of brain-related problems, the need for brain imaging has never been greater. Therefore, the modern medical field has sought to build on these historical approaches by developing imaging techniques that reduce the risk of losing life, but at the same time increase accuracy and the amount of information gleaned. One such technique in modern neurosurgery and neuroimaging is cerebral angiography. This technique was introduced by Egas Moniz, a neurologist based in Lisbon in 1927, and allows for the visualization of blood vessels in the brain. The technique ensured an enhanced sense of accuracy that its predecessors lacked. However, like ventriculography, in spite of the great strides that the technique took, the level of threat to the patients was equally dire. In this case, the technique could at times lead to cases of delirium in its patients. For this reason, there was a clear need to develop techniques that both improved performance and risk management rate. One of the greatest im- provements came in the form of computerized techniques in the late 20th 2 century and early 21st centuries. The techniques developed in this era are typ- ically referred to as computerized tomography. The first computerized brain imaging technique is the computerized axial tomography which is commonly known as CAT scanning. This technique revolutionized brain imaging and neurosurgery as features in the brain that could not previously be viewed using older techniques become visible and available for both diagnosis and further research. Unlike previous techniques, the computer-based technique allowed the physicians to administer the imaging process in a painless, effec- tive, and nearly non-invasive way. This meant that the technique posed less risk compared to the earlier techniques. During the same period, radioactive neuroimaging also gained in popularity. Here technicians employed the used of photon emission tomography to conduct brain imaging. However, repeated use of this technique exposed the patients to potential radiation poisoning. As a result, a shift was made towards magnetic resonance imaging (MRI). This technique used the variation in the signals produced by protons in the body when the human head is placed next to a strong magnetic field to produce brain images. According to Hudd et al., 2019, MRI poses a risk to patients with metallic implants on their bodies such as hip implants and pacemakers. Furthermore, the technique has a claustrophobic characteristic that may be overwhelming to claustrophobic patients. However, the technique dramati- cally reduced the risks posed by the predecessors. In addition, it was found that the technique was also able to measure blood flow changes. As such, functional magnetic resonance imaging (fMRI) became the prevalent technique used to conduct functional brain imaging studies. 3 1.1.2 Multivariate pattern analysis of fMRI data To date, the majority of fMRI studies have used the so-called brain mapping approach. Here, the goal is to identify which brain areas encode a particular psychological condition or other outcome. The statistical procedures used to develop brain maps seek to test whether there is a non-zero effect of a particular psychological manipulation or observed behavior on one or more brain voxels or regions. More recently, the field of neuroimaging has begun to move away from the traditional brain mapping approach towards the development of integrated, multivariate brain models of mental events. Multivariate Pat- tern Analysis (MVPA), brain decoding, and machine learning are terms used to describe overlapping subsets of these models and the algorithms used to develop them.

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